Recently, reconfigurable intelligent surface (RIS), as a promising technology of 6G wireless networks, has been proposed to enhance the signal quality at desired receivers [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. Specifically, an RIS is able to establish favourable channel responses by customizing the wireless propagation environment via a large number of reconfigurable passive reflecting elements. Thus, the deployment of RIS can enhance the strength of the signals between the BS and the devices in dead zones via bypassing the obstacle between them. Moreover, by optimizing the phase shifts, the signal propagation between the BS and the devices can be smartly reconfigured to improve the accuracy of active device separation.
On the other hand, the performance gain in RIS-aided communication systems depends critically on the availability of channel state information (CSI). Yet, CSI acquisition is quite challenging in practice due to the passive nature of RIS. In general, RIS without equipping active radio frequency chains can neither transmit nor receive pilot signals, thus it is challenging to estimate the channels from RIS to the BS
and devices to RIS separately. Instead, the concatenated device-RIS-BS channels are usually estimated based on the pilot sequences sent from the devices. For example, by exploiting the information on the slow-varying channel components and the hidden channel sparsity, [7]} formulated the channel estimation problem for RIS-aided multiuser MIMO systems as a matrix-calibration based matrix factorization task. Besides, to enable massive random access, [8]} proposed a three-stage framework based on the approximate message passing (AMP) for joint active device separation and channel estimation in RIS-aided sourced random access.
